Black reformating

pathways/lca.py

@@ -3,12 +3,12 @@
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
+import bw_processing as bwp
+import numpy as np
 import scipy.sparse
 import xarray as xr
 from scipy import sparse
 from scipy.sparse import csr_matrix
-import bw_processing as bwp
-import numpy as np
 
 from .lcia import get_lcia_methods
 
@@ -149,13 +149,13 @@ def get_lca_matrices(
     )
 
     dp.add_persistent_vector(
-        matrix='technosphere_matrix',
+        matrix="technosphere_matrix",
         indices_array=a_indices,
         data_array=a_data,
         flip_array=a_sign,
     )
     dp.add_persistent_vector(
-        matrix='biosphere_matrix',
+        matrix="biosphere_matrix",
         indices_array=b_indices,
         data_array=b_data,
         flip_array=b_sign,
@@ -185,10 +185,7 @@ def get_lca_matrices(
     return dp, A_inds, B_inds
 
 
-def fill_characterization_factors_matrix(
-        biosphere_flows: dict,
-        methods
-) -> np.ndarray:
+def fill_characterization_factors_matrix(biosphere_flows: dict, methods) -> np.ndarray:
     """
     Create a characterization matrix based on the list of biosphere flows
     given.
@@ -201,7 +198,6 @@ def fill_characterization_factors_matrix(
 
     print(lcia_data)
 
-
     # create a numpy array filled with zeros
     # of size equal to biosphere_flows and lcia methods
 
@@ -217,6 +213,7 @@ def fill_characterization_factors_matrix(
 
     return cf_matrix
 
+
 def remove_double_counting(A: csr_matrix, vars_info: dict) -> csr_matrix:
     """
     Remove double counting from a technosphere matrix.

pathways/lcia.py

@@ -45,6 +45,3 @@ def get_lcia_methods(methods: list = None):
         data = [x for x in data if " - ".join(x["name"]) in methods]
 
     return {" - ".join(x["name"]): format_lcia_method_exchanges(x) for x in data}
-
-
-

pathways/pathways.py

@@ -10,14 +10,14 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import bw2calc as bc
 import numpy as np
 import pandas as pd
 import pyprind
 import xarray as xr
 import yaml
 from datapackage import DataPackage
 from premise.geomap import Geomap
-import bw2calc as bc
 
 from . import DATA_DIR
 from .data_validation import validate_datapackage
@@ -309,76 +309,74 @@ def process_region(data: Tuple) -> Union[None, Dict[str, Any]]:
     lca.lcia()
     lca.score
 
-
-
-        #
-        #
-        # # Create the demand vector
-        # f = create_demand_vector([idx], A, demand, unit_vector)
-        #
-        # # Solve the inventory
-        # C = solve_inventory(A, B, f)
-        #
-        # if lcia_matrix is not None:
-        #     if lcia_matrix.ndim != 2 or lcia_matrix.shape[0] != B.shape[1]:
-        #         raise ValueError("Incompatible dimensions between B and lcia_matrix")
-        #
-        #     # Solve the LCA problem to get the LCIA scores
-        #     D = characterize_inventory(C, lcia_matrix)
-        #
-        #     # Sum along the first axis of D to get final result
-        #     D = D.sum(axis=1)
-        #
-        #     # Initialize the new array with zeros for missing data
-        #     E = np.zeros((len(A_index), len(locations), len(impact_categories)))
-        #
-        #     # Populate the result array
-        #     act_locs = [a[-1] for a in rev_A_index.values()]
-        #
-        #     for i, act in enumerate(rev_A_index.values()):
-        #         if act[-1] in act_locs:
-        #             loc_idx = location_to_index[act[-1]]
-        #             E[i, loc_idx, :] = D[i, :]
-        #
-        #     acts_idx = generate_A_indices(
-        #         A_index,
-        #         reverse_classifications,
-        #         lca_results_coords,
-        #     )
-        #
-        #     # Sum over the first axis of D,
-        #     # using acts_idx for advanced indexing
-        #     target[:, v] = E[acts_idx, ...].sum(axis=0)
-        #
-        # else:
-        #     # else, just sum the results of the inventory
-        #     acts_idx = generate_A_indices(
-        #         A_index,
-        #         reverse_classifications,
-        #         lca_results_coords,
-        #     )
-        #     if flows is not None:
-        #
-        #         def transf_flow(f):
-        #             return tuple(f.split(" - "))
-        #
-        #         flows_idx = [int(B_index[transf_flow(f)]) for f in flows]
-        #         C = C[:, flows_idx]
-        #
-        #         # Initialize the new array with zeros for missing data
-        #         E = np.zeros((len(A_index), len(locations), len(flows_idx)))
-        #
-        #         # Populate the result array
-        #         act_locs = [a[-1] for a in rev_A_index.values()]
-        #
-        #         for i, act in enumerate(rev_A_index.values()):
-        #             if act[-1] in act_locs:
-        #                 loc_idx = location_to_index[act[-1]]
-        #                 E[i, loc_idx, :] = C[i, :]
-        #
-        #         target[:, v] = E[acts_idx, ...].sum(axis=0)
-        #     else:
-        #         target[:, v] = C[acts_idx, ...].sum(axis=0)
+    #
+    #
+    # # Create the demand vector
+    # f = create_demand_vector([idx], A, demand, unit_vector)
+    #
+    # # Solve the inventory
+    # C = solve_inventory(A, B, f)
+    #
+    # if lcia_matrix is not None:
+    #     if lcia_matrix.ndim != 2 or lcia_matrix.shape[0] != B.shape[1]:
+    #         raise ValueError("Incompatible dimensions between B and lcia_matrix")
+    #
+    #     # Solve the LCA problem to get the LCIA scores
+    #     D = characterize_inventory(C, lcia_matrix)
+    #
+    #     # Sum along the first axis of D to get final result
+    #     D = D.sum(axis=1)
+    #
+    #     # Initialize the new array with zeros for missing data
+    #     E = np.zeros((len(A_index), len(locations), len(impact_categories)))
+    #
+    #     # Populate the result array
+    #     act_locs = [a[-1] for a in rev_A_index.values()]
+    #
+    #     for i, act in enumerate(rev_A_index.values()):
+    #         if act[-1] in act_locs:
+    #             loc_idx = location_to_index[act[-1]]
+    #             E[i, loc_idx, :] = D[i, :]
+    #
+    #     acts_idx = generate_A_indices(
+    #         A_index,
+    #         reverse_classifications,
+    #         lca_results_coords,
+    #     )
+    #
+    #     # Sum over the first axis of D,
+    #     # using acts_idx for advanced indexing
+    #     target[:, v] = E[acts_idx, ...].sum(axis=0)
+    #
+    # else:
+    #     # else, just sum the results of the inventory
+    #     acts_idx = generate_A_indices(
+    #         A_index,
+    #         reverse_classifications,
+    #         lca_results_coords,
+    #     )
+    #     if flows is not None:
+    #
+    #         def transf_flow(f):
+    #             return tuple(f.split(" - "))
+    #
+    #         flows_idx = [int(B_index[transf_flow(f)]) for f in flows]
+    #         C = C[:, flows_idx]
+    #
+    #         # Initialize the new array with zeros for missing data
+    #         E = np.zeros((len(A_index), len(locations), len(flows_idx)))
+    #
+    #         # Populate the result array
+    #         act_locs = [a[-1] for a in rev_A_index.values()]
+    #
+    #         for i, act in enumerate(rev_A_index.values()):
+    #             if act[-1] in act_locs:
+    #                 loc_idx = location_to_index[act[-1]]
+    #                 E[i, loc_idx, :] = C[i, :]
+    #
+    #         target[:, v] = E[acts_idx, ...].sum(axis=0)
+    #     else:
+    #         target[:, v] = C[acts_idx, ...].sum(axis=0)
 
     # Return a dictionary containing the processed LCA data for the given region
     def get_indices():

setup.py

@@ -49,7 +49,7 @@ def package_files(directory):
         "scipy",
         "premise",
         "pyyaml",
-        "scikit-umfpack"
+        "scikit-umfpack",
     ],
     url="https://github.com/polca/premise",
     description="Scenario-level LCA of energy systems and transition pathways",